352 lines
27 KiB
Plaintext
352 lines
27 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "98cddc6a-2ce1-4933-a2b7-96d2c2d197f4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/javascript": [
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"if (window.IPython && IPython.notebook.kernel) IPython.notebook.kernel.execute('jovian.utils.jupyter.get_notebook_name_saved = lambda: \"' + IPython.notebook.notebook_name + '\"')"
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],
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"text/plain": [
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"<IPython.core.display.Javascript object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import torch\n",
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"import jovian\n",
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"import torchvision\n",
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"import matplotlib\n",
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"import torch.nn as nn\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import torch.nn.functional as F\n",
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"from torchvision.datasets.utils import download_url\n",
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"from torch.utils.data import DataLoader, TensorDataset, random_split\n",
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"import random\n",
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"import os\n",
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"import sys\n",
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"from sklearn.metrics import mean_squared_error\n",
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"from sklearn.metrics import mean_absolute_error"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "7bb63556-d009-4d9f-9de0-033a30ad3fc4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(['matches', 'wins', 'draws', 'loses', 'scored', 'missed', 'pts'],\n",
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" ['position'])"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#load data\n",
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"dataframe = pd.read_csv(\"understat.csv\")\n",
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"\n",
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"#choose columns\n",
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"input_cols=list(dataframe.columns)[4:11]\n",
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"output_cols = ['position']\n",
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"input_cols, output_cols"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "c8151c46-c234-42b7-a786-50c73e3aa2f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"def dataframe_to_arrays(dataframe):\n",
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" dataframe_loc = dataframe.copy(deep=True)\n",
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" inputs_array = dataframe_loc[input_cols].to_numpy()\n",
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" targets_array = dataframe_loc[output_cols].to_numpy()\n",
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" return inputs_array, targets_array\n",
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"\n",
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"inputs_array, targets_array = dataframe_to_arrays(dataframe)\n",
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"\n",
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"inputs = torch.from_numpy(inputs_array).type(torch.float)\n",
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"targets = torch.from_numpy(targets_array).type(torch.float)\n",
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"\n",
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"dataset = TensorDataset(inputs, targets)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "8c89947b-c2fe-407d-9588-3f0087df5955",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_ds, val_ds = random_split(dataset, [548, 136])\n",
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"batch_size=50\n",
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"train_loader = DataLoader(train_ds, batch_size, shuffle=True)\n",
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"val_loader = DataLoader(val_ds, batch_size)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "3b1426a0-5b15-46f8-aea9-871462ca9467",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Model_xPosition(nn.Module):\n",
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" def __init__(self):\n",
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" super().__init__()\n",
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" self.linear = nn.Linear(input_size,output_size) \n",
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" \n",
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" def forward(self, xb): \n",
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" out = self.linear(xb)\n",
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" return out\n",
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" \n",
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" def training_step(self, batch):\n",
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" inputs, targets = batch \n",
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" # Generate predictions\n",
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" out = self(inputs) \n",
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" # Calcuate loss\n",
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" loss = F.l1_loss(out,targets) \n",
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" return loss\n",
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" \n",
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" def validation_step(self, batch):\n",
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" inputs, targets = batch\n",
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" out = self(inputs)\n",
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" loss = F.l1_loss(out,targets) \n",
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" return {'val_loss': loss.detach()}\n",
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" \n",
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" def validation_epoch_end(self, outputs):\n",
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" batch_losses = [x['val_loss'] for x in outputs]\n",
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" epoch_loss = torch.stack(batch_losses).mean() \n",
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" return {'val_loss': epoch_loss.item()}\n",
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" \n",
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" def epoch_end(self, epoch, result, num_epochs):\n",
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" if (epoch+1) % 100 == 0 or epoch == num_epochs-1:\n",
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" print(\"Epoch {} loss: {:.4f}\".format(epoch+1, result['val_loss']))\n",
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" \n",
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" \n",
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"def evaluate(model, val_loader):\n",
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" outputs = [model.validation_step(batch) for batch in val_loader]\n",
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" return model.validation_epoch_end(outputs)\n",
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"\n",
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"def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):\n",
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" history = []\n",
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" optimizer = opt_func(model.parameters(), lr)\n",
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" for epoch in range(epochs):\n",
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" for batch in train_loader:\n",
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" loss = model.training_step(batch)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" optimizer.zero_grad()\n",
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" result = evaluate(model, val_loader)\n",
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" model.epoch_end(epoch, result, epochs)\n",
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" history.append(result)\n",
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" return history"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "f2e22e9a-8724-4084-b706-0be266846c05",
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"metadata": {},
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"outputs": [],
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"source": [
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"input_size = len(input_cols)\n",
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"output_size = len(output_cols)\n",
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"model=Model_xPosition()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "efacafe4-797a-4588-b0d8-2e4d883e639a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 100 loss: 2.2152\n",
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"Epoch 200 loss: 1.8737\n",
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"Epoch 300 loss: 1.8362\n",
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"Epoch 400 loss: 1.7904\n",
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"Epoch 500 loss: 1.7507\n",
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"Epoch 600 loss: 1.7174\n",
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"Epoch 700 loss: 1.6977\n",
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"Epoch 800 loss: 1.6847\n",
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"Epoch 900 loss: 1.6743\n",
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"Epoch 1000 loss: 1.6645\n"
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]
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}
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],
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"source": [
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"epochs = 1000\n",
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"lr = 1e-5\n",
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"learning_proccess = fit(epochs, lr, model, train_loader, val_loader)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "7007ab5a-dc79-4321-beed-cd54dd197858",
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"metadata": {},
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"outputs": [],
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"source": [
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"def predict_single(input, target, model):\n",
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" inputs = input.unsqueeze(0)\n",
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" predictions = model(inputs)\n",
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" prediction = predictions[0].detach()\n",
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"\n",
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" return \"Target: \"+str(target)+\" Predicted: \"+str(prediction)+\"\\n\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "c946417a-693a-463a-b123-54348266ff6e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def prediction(input, target, model):\n",
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" inputs = input.unsqueeze(0)\n",
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" predictions = model(inputs)\n",
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" predicted = predictions[0].detach()\n",
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" return predicted"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "50c62065-5094-4595-995c-6d0b71f1f28a",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"result.txt\", \"a+\") as file:\n",
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" for i in range(0, len(val_ds), 1):\n",
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" input_, target = val_ds[i]\n",
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" file.write(str(predict_single(input_, target, model)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "fdd6468c-3d50-4131-b2d2-e3190d8b19e5",
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"metadata": {},
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"outputs": [],
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"source": [
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"expected = []\n",
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"predicted = []\n",
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"for i in range(0, len(val_ds), 1):\n",
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" input_, target = val_ds[i]\n",
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" expected.append(float(target))\n",
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" predicted.append(float(prediction(input_, target, model)))\n",
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"\n",
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"MSE = mean_squared_error(expected, predicted)\n",
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"MAE = mean_absolute_error(expected, predicted)\n",
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"\n",
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"with open(\"metrics.txt\", \"a+\") as file:\n",
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" file.write(\"Mean squared error: MSE = \"+ str(MSE) + \"\\n\")\n",
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" file.write(\"Mean absolute error: MAE = \"+ str(MAE)+ \"\\n\")\n",
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"\n",
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"with open(\"MSE.txt\", \"a+\") as file:\n",
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" file.write(str(MSE) + \"\\n\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "4a1e5b16-7d80-47b0-8313-e44667687779",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('MSE.txt') as file:\n",
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" y_MSE = [float(line) for line in file if line]\n",
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" x_builds = list(range(1, len(y_MSE) + 1))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "1da02242-846a-499c-92eb-4c80fe5f43c4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 0 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.xlabel('Number of builds')\n",
|
||
|
|
"plt.ylabel('MSE')\n",
|
||
|
|
"plt.plot(x_builds, y_MSE, label='Mean squared error')\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"plt.savefig('RMSplot.png')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "8dffe789-1ad5-44f1-8f21-92b9c89ed974",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"!jupyter nbconvert --to script ml_pytorch.ipynb"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.9.7"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|